Log-linear RDiT - MA Data Analysis
Fomula
Scatter plot
Food waste plots
Log-Linear RDiT Model
No Interaction
##
## Call:
## lm(formula = lm_fw_log, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7027 -0.2132 -0.0200 0.2094 0.9011
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1076634 0.2288928 -0.470 0.63879
## D_01 0.0836141 0.1162162 0.719 0.47299
## t_01 0.0002004 0.0013436 0.149 0.88161
## temp_c 0.0011001 0.0033687 0.327 0.74446
## humi_p 0.0017097 0.0024305 0.703 0.48289
## prcp_mm -0.0204790 0.0129101 -1.586 0.11482
## liquors 0.0088720 0.0170388 0.521 0.60336
## sales 0.0012101 0.0001842 6.568 8.23e-10 ***
## halfs 0.0304686 0.0092111 3.308 0.00118 **
## tueE 0.0828020 0.0603831 1.371 0.17238
## wedE -0.0553904 0.0599979 -0.923 0.35741
## thuE -0.0941898 0.0580164 -1.624 0.10662
## friE 0.0234732 0.0570261 0.412 0.68122
## satE -0.0810580 0.0598807 -1.354 0.17792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3199 on 147 degrees of freedom
## Multiple R-squared: 0.5572, Adjusted R-squared: 0.5181
## F-statistic: 14.23 on 13 and 147 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = lm_sfw_log, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41716 -0.15237 -0.03535 0.12801 0.87712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1068077 0.1501000 -0.712 0.4779
## container 0.0321430 0.0767189 0.419 0.6758
## time -0.0004798 0.0008872 -0.541 0.5894
## temp_c -0.0013782 0.0022094 -0.624 0.5337
## humi_p 0.0004059 0.0016033 0.253 0.8005
## prcp_mm -0.0110047 0.0085030 -1.294 0.1976
## liquors 0.0067392 0.0111983 0.602 0.5482
## sales 0.0007082 0.0001211 5.848 3.1e-08 ***
## halfs 0.0098236 0.0060478 1.624 0.1065
## tueE 0.0812933 0.0396422 2.051 0.0421 *
## wedE -0.0099471 0.0394169 -0.252 0.8011
## thuE -0.0717368 0.0381106 -1.882 0.0618 .
## friE 0.0170329 0.0374754 0.455 0.6501
## satE -0.0518111 0.0393260 -1.317 0.1897
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2101 on 147 degrees of freedom
## Multiple R-squared: 0.4363, Adjusted R-squared: 0.3864
## F-statistic: 8.75 on 13 and 147 DF, p-value: 4.722e-13##
## Call:
## lm(formula = lm_lfw_log, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6306 -0.2071 -0.0039 0.2034 0.7178
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2202379 0.2042502 -1.078 0.282677
## container 0.1676759 0.1043961 1.606 0.110387
## time -0.0003381 0.0012072 -0.280 0.779828
## temp_c 0.0010945 0.0030065 0.364 0.716357
## humi_p 0.0013839 0.0021817 0.634 0.526859
## prcp_mm -0.0152197 0.0115706 -1.315 0.190431
## liquors 0.0062013 0.0152383 0.407 0.684634
## sales 0.0010214 0.0001648 6.199 5.46e-09 ***
## halfs 0.0295618 0.0082296 3.592 0.000447 ***
## tueE 0.0247009 0.0539435 0.458 0.647698
## wedE -0.0566017 0.0536369 -1.055 0.293031
## thuE -0.0756933 0.0518594 -1.460 0.146538
## friE 0.0353354 0.0509951 0.693 0.489454
## satE -0.0580457 0.0535134 -1.085 0.279832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2859 on 147 degrees of freedom
## Multiple R-squared: 0.561, Adjusted R-squared: 0.5221
## F-statistic: 14.45 on 13 and 147 DF, p-value: < 2.2e-16Interaction
##
## Call:
## lm(formula = rdt_int_fw_log, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.07089 -0.29053 0.07099 0.31294 0.92461
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.899186 0.100117 8.981 7.9e-16 ***
## container 0.125174 0.145241 0.862 0.3901
## time -0.003624 0.001999 -1.813 0.0717 .
## container:time 0.004636 0.003166 1.464 0.1451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4602 on 157 degrees of freedom
## Multiple R-squared: 0.02157, Adjusted R-squared: 0.002876
## F-statistic: 1.154 on 3 and 157 DF, p-value: 0.3293##
## Call:
## lm(formula = rdt_int_sfw_log, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.49626 -0.17125 -0.02458 0.17911 0.84798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.456958 0.058104 7.864 5.59e-13 ***
## container -0.002367 0.084292 -0.028 0.978
## time -0.001638 0.001160 -1.412 0.160
## container:time 0.001864 0.001837 1.015 0.312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2671 on 157 degrees of freedom
## Multiple R-squared: 0.0273, Adjusted R-squared: 0.008717
## F-statistic: 1.469 on 3 and 157 DF, p-value: 0.2251##
## Call:
## lm(formula = rdt_int_lfw_log, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86306 -0.32547 0.08476 0.29523 0.83820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.647578 0.089499 7.236 1.93e-11 ***
## container 0.172827 0.129837 1.331 0.1851
## time -0.003520 0.001787 -1.970 0.0506 .
## container:time 0.004447 0.002830 1.572 0.1181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4114 on 157 degrees of freedom
## Multiple R-squared: 0.02936, Adjusted R-squared: 0.01082
## F-statistic: 1.583 on 3 and 157 DF, p-value: 0.1956Ass-Interaction
- Linearity of the relationships between the dependent and independent variables
- Normality of the residuals
- Homoscedasticity of the residuals
- No influential points (outliers)
- No multicollinearity
- Independence of the observations
## OK: Error variance appears to be homoscedastic (p = 0.654).## OK: Error variance appears to be homoscedastic (p = 0.064).## OK: Error variance appears to be homoscedastic (p = 0.778).## Warning: Non-normality of residuals detected (p = 0.014).## Warning: Non-normality of residuals detected (p = 0.044).## Warning: Non-normality of residuals detected (p = 0.004).##
## studentized Breusch-Pagan test
##
## data: rdt_int_fw_log
## BP = 1.4669, df = 3, p-value = 0.6899##
## studentized Breusch-Pagan test
##
## data: rdt_int_sfw_log
## BP = 3.892, df = 3, p-value = 0.2734##
## studentized Breusch-Pagan test
##
## data: rdt_int_lfw_log
## BP = 2.2738, df = 3, p-value = 0.5176## OK: No outliers detected.
## - Based on the following method and threshold: cook (0.8).
## - For variable: (Whole model)## OK: No outliers detected.
## - Based on the following method and threshold: cook (0.8).
## - For variable: (Whole model)## OK: No outliers detected.
## - Based on the following method and threshold: cook (0.8).
## - For variable: (Whole model)## OK: Residuals appear to be independent and not autocorrelated (p = 0.662).## OK: Residuals appear to be independent and not autocorrelated (p = 0.622).## OK: Residuals appear to be independent and not autocorrelated (p = 0.582).
Multiple model
##
## Call:
## lm(formula = rdt_fw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.70866 -0.21590 -0.01625 0.20922 0.89888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1692730 0.3090121 -0.548 0.58467
## D_01 0.0845725 0.1166225 0.725 0.46950
## t_01 -0.0002663 0.0020667 -0.129 0.89765
## temp_c 0.0004440 0.0040335 0.110 0.91250
## humi_p 0.0022324 0.0030037 0.743 0.45855
## prcp_mm -0.0205960 0.0129562 -1.590 0.11407
## tueE 0.0817523 0.0606736 1.347 0.17994
## wedE -0.0556663 0.0601918 -0.925 0.35659
## thuE -0.0927076 0.0584093 -1.587 0.11463
## friE 0.0234423 0.0572038 0.410 0.68255
## satE -0.0814800 0.0600838 -1.356 0.17716
## liquors 0.0090819 0.0171063 0.531 0.59629
## sales 0.0012053 0.0001855 6.498 1.2e-09 ***
## halfs 0.0309808 0.0093984 3.296 0.00123 **
## D_01:t_01 0.0009565 0.0032107 0.298 0.76620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3209 on 146 degrees of freedom
## Multiple R-squared: 0.5575, Adjusted R-squared: 0.5151
## F-statistic: 13.14 on 14 and 146 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = rdt_sfw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.41319 -0.15182 -0.03526 0.12545 0.87727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0803500 0.2030080 -0.396 0.6928
## D_01 0.0294107 0.0766161 0.384 0.7016
## t_01 -0.0002585 0.0013577 -0.190 0.8493
## temp_c -0.0010746 0.0026499 -0.406 0.6857
## humi_p 0.0002130 0.0019733 0.108 0.9142
## prcp_mm -0.0110992 0.0085117 -1.304 0.1943
## tueE 0.0824693 0.0398600 2.069 0.0403 *
## wedE -0.0102507 0.0395435 -0.259 0.7958
## thuE -0.0725057 0.0383725 -1.890 0.0608 .
## friE 0.0168294 0.0375805 0.448 0.6549
## satE -0.0516508 0.0394726 -1.309 0.1928
## liquors 0.0065888 0.0112381 0.586 0.5586
## sales 0.0007091 0.0001219 5.819 3.6e-08 ***
## halfs 0.0095723 0.0061743 1.550 0.1232
## D_01:t_01 -0.0004032 0.0021093 -0.191 0.8487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2108 on 146 degrees of freedom
## Multiple R-squared: 0.4363, Adjusted R-squared: 0.3823
## F-statistic: 8.072 on 14 and 146 DF, p-value: 1.432e-12##
## Call:
## lm(formula = rdt_lfw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.64722 -0.20047 0.00199 0.20121 0.72696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2995617 0.2773779 -1.080 0.281933
## D_01 0.1093355 0.1046836 1.044 0.298009
## t_01 -0.0004948 0.0018551 -0.267 0.790068
## temp_c 0.0008000 0.0036206 0.221 0.825438
## humi_p 0.0025121 0.0026962 0.932 0.353026
## prcp_mm -0.0164768 0.0116299 -1.417 0.158681
## tueE 0.0259768 0.0544623 0.477 0.634098
## wedE -0.0592020 0.0540299 -1.096 0.275002
## thuE -0.0743597 0.0524298 -1.418 0.158243
## friE 0.0330101 0.0513477 0.643 0.521314
## satE -0.0581687 0.0539329 -1.079 0.282573
## liquors 0.0055564 0.0153551 0.362 0.717978
## sales 0.0010056 0.0001665 6.040 1.22e-08 ***
## halfs 0.0303365 0.0084362 3.596 0.000441 ***
## D_01:t_01 0.0015928 0.0028821 0.553 0.581341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2881 on 146 degrees of freedom
## Multiple R-squared: 0.5574, Adjusted R-squared: 0.515
## F-statistic: 13.13 on 14 and 146 DF, p-value: < 2.2e-16## Warning: To compile a LaTeX document with this table, the following commands must be placed in the document preamble:
##
## \usepackage{booktabs}
## \usepackage{siunitx}
## \newcolumntype{d}{S[
## input-open-uncertainty=,
## input-close-uncertainty=,
## parse-numbers = false,
## table-align-text-pre=false,
## table-align-text-post=false
## ]}
##
## To disable `siunitx` and prevent `modelsummary` from wrapping numeric entries in `\num{}`, call:
##
## options("modelsummary_format_numeric_latex" = "plain")
## This warning appears once per session.Ass-Multiple
- Independence of the observations
- Normality of the residuals
- No influential points (outliers)
- Homoscedasticity of the residuals
- Linearity of the relationships between the dependent and independent variables
- No multicollinearity
## Warning in adf.test(df$log_food_waste_kg): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: df$log_food_waste_kg
## Dickey-Fuller = -5.7678, Lag order = 5, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(df$log_solid_waste_kg): p-value smaller than printed
## p-value##
## Augmented Dickey-Fuller Test
##
## data: df$log_solid_waste_kg
## Dickey-Fuller = -6.8741, Lag order = 5, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(df$log_liquid_waste_kg): p-value smaller than printed
## p-value##
## Augmented Dickey-Fuller Test
##
## data: df$log_liquid_waste_kg
## Dickey-Fuller = -5.0025, Lag order = 5, p-value = 0.01
## alternative hypothesis: stationary## OK: Residuals appear to be independent and not autocorrelated (p = 0.716).## OK: Residuals appear to be independent and not autocorrelated (p = 0.556).## OK: Residuals appear to be independent and not autocorrelated (p = 0.596).## lag Autocorrelation D-W Statistic p-value
## 1 -0.01230935 1.983789 0.708
## Alternative hypothesis: rho != 0## lag Autocorrelation D-W Statistic p-value
## 1 -0.01340069 1.960017 0.634
## Alternative hypothesis: rho != 0## lag Autocorrelation D-W Statistic p-value
## 1 0.001818417 1.965806 0.628
## Alternative hypothesis: rho != 0##
## Durbin-Watson test
##
## data: rdt_of$`food waste`
## DW = 1.9838, p-value = 0.3499
## alternative hypothesis: true autocorrelation is greater than 0##
## Durbin-Watson test
##
## data: rdt_of$`solid waste`
## DW = 1.96, p-value = 0.2963
## alternative hypothesis: true autocorrelation is greater than 0##
## Durbin-Watson test
##
## data: rdt_of$`liquid waste`
## DW = 1.9658, p-value = 0.309
## alternative hypothesis: true autocorrelation is greater than 0## OK: residuals appear as normally distributed (p = 0.740).## Warning: Non-normality of residuals detected (p < .001).## OK: residuals appear as normally distributed (p = 0.755).## OK: No outliers detected.
## - Based on the following method and threshold: cook (1).
## - For variable: (Whole model)## OK: No outliers detected.
## - Based on the following method and threshold: cook (1).
## - For variable: (Whole model)## OK: No outliers detected.
## - Based on the following method and threshold: cook (1).
## - For variable: (Whole model)##
## studentized Breusch-Pagan test
##
## data: rdt_of$`food waste`
## BP = 19.527, df = 14, p-value = 0.1458##
## studentized Breusch-Pagan test
##
## data: rdt_of$`solid waste`
## BP = 12.644, df = 14, p-value = 0.5548##
## studentized Breusch-Pagan test
##
## data: rdt_of$`liquid waste`
## BP = 11.792, df = 14, p-value = 0.623## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.015).## OK: Error variance appears to be homoscedastic (p = 0.446).## OK: Error variance appears to be homoscedastic (p = 0.152).## Model has interaction terms. VIFs might be inflated.
## You may check multicollinearity among predictors of a model without
## interaction terms.## # Check for Multicollinearity
##
## Low Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## temp_c 2.27 [ 1.87, 2.87] 1.51 0.44 [0.35, 0.54]
## humi_p 1.97 [ 1.64, 2.48] 1.40 0.51 [0.40, 0.61]
## prcp_mm 1.19 [ 1.07, 1.52] 1.09 0.84 [0.66, 0.93]
## tueE 1.86 [ 1.55, 2.32] 1.36 0.54 [0.43, 0.64]
## wedE 1.83 [ 1.53, 2.29] 1.35 0.55 [0.44, 0.65]
## thuE 1.66 [ 1.40, 2.07] 1.29 0.60 [0.48, 0.71]
## friE 1.65 [ 1.40, 2.06] 1.28 0.61 [0.49, 0.72]
## satE 1.79 [ 1.50, 2.23] 1.34 0.56 [0.45, 0.67]
## liquors 1.53 [ 1.31, 1.91] 1.24 0.65 [0.52, 0.76]
## sales 2.46 [ 2.01, 3.11] 1.57 0.41 [0.32, 0.50]
## halfs 1.46 [ 1.26, 1.82] 1.21 0.68 [0.55, 0.79]
##
## Moderate Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## D_01 5.28 [ 4.15, 6.82] 2.30 0.19 [0.15, 0.24]
## D_01:t_01 9.01 [ 6.97, 11.73] 3.00 0.11 [0.09, 0.14]
##
## High Correlation
##
## Term VIF VIF 95% CI Increased SE Tolerance Tolerance 95% CI
## t_01 14.42 [11.08, 18.87] 3.80 0.07 [0.05, 0.09]
Comparison RDiT and MANOVA
Local log-linear Regression
Interaction - b/w week and month
## [1] 6.03181## [1] 4.095261## [1] 5.845282
Covariates continuity test
Placebo test
| cutoff_1101 | cutoff_1203 | cutoff_0103 | cutoff_0201 | cutoff_0301 | |
|---|---|---|---|---|---|
| D (fw) | 0.946 | 0.910 | 1.088 | 1.132 | 1.099 |
| s.e. = 0.137 | s.e. = 0.101 | s.e. = 0.127 | s.e. = 0.132 | s.e. = 0.165 | |
| D × t (fw) | 0.999 | 1.001 | 1.001 | 0.996 | 0.991 |
| s.e. = 0.005 | s.e. = 0.003 | s.e. = 0.003 | s.e. = 0.004 | s.e. = 0.009 |
| cutoff_1101 | cutoff_1203 | cutoff_0103 | cutoff_0201 | cutoff_0301 | |
|---|---|---|---|---|---|
| D (solid) | 1.024 | 0.891 | 1.030 | 1.060 | 1.077 |
| s.e. = 0.096 | s.e. = 0.064 | s.e. = 0.079 | s.e. = 0.081 | s.e. = 0.106 | |
| D × t (solid) | 0.996 | 0.998 | 1.000 | 0.998 | 0.994 |
| s.e. = 0.003 | s.e. = 0.002 | s.e. = 0.002 | s.e. = 0.003 | s.e. = 0.006 |
| cutoff_1101 | cutoff_1203 | cutoff_0103 | cutoff_0201 | cutoff_0301 | |
|---|---|---|---|---|---|
| D (liquid) | 0.881 | 0.994 | 1.116 | 1.136 | 1.044 |
| s.e. = 0.114 | s.e. = 0.099 | s.e. = 0.117 | s.e. = 0.119 | s.e. = 0.141 | |
| D × t (liquid) | 1.001 | 1.003 | 1.002 | 0.997 | 0.993 |
| s.e. = 0.004 | s.e. = 0.003 | s.e. = 0.003 | s.e. = 0.003 | s.e. = 0.008 |
Donut hole test
Food waste
### Solid Food waste
Liquid food waste
log-linear Multiple - month
##
## Call:
## lm(formula = log_food_waste_kg ~ container * time + temp_c +
## humi_p + prcp_mm + tueE + wedE + thuE + friE + satE + liquors +
## sales + halfs, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51357 -0.16849 0.01946 0.12329 0.61325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2988962 0.6895540 0.433 0.66726
## container 0.0751821 0.1844288 0.408 0.68595
## time -0.0035192 0.0090010 -0.391 0.69812
## temp_c 0.0069893 0.0076565 0.913 0.36739
## humi_p -0.0036874 0.0071753 -0.514 0.61047
## prcp_mm -0.0482459 0.0274499 -1.758 0.08732 .
## tueE 0.1637430 0.1050361 1.559 0.12776
## wedE 0.0131842 0.1098702 0.120 0.90515
## thuE -0.0631627 0.0994321 -0.635 0.52929
## friE 0.0006048 0.0987478 0.006 0.99515
## satE -0.1848926 0.1050318 -1.760 0.08684 .
## liquors 0.0040398 0.0296279 0.136 0.89230
## sales 0.0012807 0.0003682 3.478 0.00134 **
## halfs 0.0303484 0.0196676 1.543 0.13156
## container:time 0.0092436 0.0137591 0.672 0.50599
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3018 on 36 degrees of freedom
## Multiple R-squared: 0.6852, Adjusted R-squared: 0.5627
## F-statistic: 5.596 on 14 and 36 DF, p-value: 1.496e-05##
## Call:
## lm(formula = log_solid_waste_kg ~ container * time + temp_c +
## humi_p + prcp_mm + tueE + wedE + thuE + friE + satE + liquors +
## sales + halfs, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23587 -0.09713 -0.00118 0.07621 0.37761
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2503976 0.3792951 0.660 0.513350
## container 0.0017171 0.1014466 0.017 0.986589
## time -0.0009035 0.0049511 -0.182 0.856225
## temp_c 0.0076969 0.0042115 1.828 0.075913 .
## humi_p -0.0035071 0.0039469 -0.889 0.380123
## prcp_mm -0.0125956 0.0150991 -0.834 0.409673
## tueE 0.0361610 0.0577760 0.626 0.535340
## wedE 0.0311853 0.0604350 0.516 0.609000
## thuE -0.0499517 0.0546935 -0.913 0.367160
## friE 0.0180888 0.0543171 0.333 0.741050
## satE -0.0788150 0.0577736 -1.364 0.180972
## liquors 0.0058179 0.0162971 0.357 0.723185
## sales 0.0007934 0.0002025 3.918 0.000383 ***
## halfs 0.0044730 0.0108184 0.413 0.681720
## container:time 0.0012049 0.0075683 0.159 0.874395
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.166 on 36 degrees of freedom
## Multiple R-squared: 0.6558, Adjusted R-squared: 0.5219
## F-statistic: 4.899 on 14 and 36 DF, p-value: 5.863e-05##
## Call:
## lm(formula = log_liquid_waste_kg ~ container * time + temp_c +
## humi_p + prcp_mm + tueE + wedE + thuE + friE + satE + liquors +
## sales + halfs, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.52081 -0.14938 0.01322 0.14151 0.59771
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0146367 0.6279620 -0.023 0.98153
## container 0.0843277 0.1679554 0.502 0.61867
## time -0.0031940 0.0081970 -0.390 0.69909
## temp_c 0.0024800 0.0069726 0.356 0.72416
## humi_p -0.0006411 0.0065344 -0.098 0.92239
## prcp_mm -0.0486593 0.0249981 -1.947 0.05943 .
## tueE 0.1400310 0.0956541 1.464 0.15189
## wedE -0.0282689 0.1000564 -0.283 0.77916
## thuE -0.0598792 0.0905507 -0.661 0.51264
## friE 0.0087874 0.0899275 0.098 0.92270
## satE -0.1478034 0.0956502 -1.545 0.13103
## liquors 0.0050597 0.0269815 0.188 0.85230
## sales 0.0009589 0.0003353 2.860 0.00701 **
## halfs 0.0333015 0.0179109 1.859 0.07118 .
## container:time 0.0099003 0.0125301 0.790 0.43463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2749 on 36 degrees of freedom
## Multiple R-squared: 0.6655, Adjusted R-squared: 0.5354
## F-statistic: 5.116 on 14 and 36 DF, p-value: 3.793e-05